epic-blacklist takes one or more ChIP-files that contain data unrelated to the experiment (ChIP from another species for example) and finds bins where a lot of reads still bind. Bins with a statistically significant number of reads are computed according to a Poisson model. These bins are written as a bed-file to stdout.

  • -i, –infiles

    One or more bed/bedpe files to count reads in.

  • -o, –outfile

    File to write results to. By default sent to stdout.

  • -cpu, –number-cores

    The number of cores epic should use. Can at most take advantage of 1 core per strand per chromosome (i.e. 46 for humans). Default: 1

  • -gn, –genome

    Which genome to analyze. By default hg19.

  • -k, –keep-duplicates

    Keep reads mapping to the same position on the same strand within a library. The default is to remove all but the first duplicate (this is done once per file, not for all files collectively.)

  • -fs, –fragment-size

    (Only used for single-end files) Size of the sequenced fragment. The center of the fragment will be used to calculate which window a read ended up in. So reads are shifted by fragment-size/2. Default 150.

  • -cs, –chromsizes

    Set the chromosome lengths yourself in a file with two columns: chromosome names and sizes. Useful to analyze custom genomes, assemblies or simulated data. Only chromosomes included in the file will be analyzed.

  • -f, –fdr

    Cut-off to consider a bin enriched (Default: 0.05)

  • -egf, –effective-genome-fraction

    Use a different effective genome fraction than the one included in epic. Or include an egf for custom genomes that are not a part of epic. Should be a number between 0 and 1. Autoinferred by sampled read-length and genome by default.